Electronic institution : an e-contracting platform for virtual organization

Automated tools that assist contract drafting are mostly focused on the representation of contract documents. Multi-agent systems have been ap-plied in the e-business domain, namely for information discovery and contract negotiation. Work on contract monitoring and enforcement is less explored. In this paper we start from these two observations to expose our efforts towards the development of tools that enable the computational representation of con-tracts and furthermore their monitoring and enforcement. We are mostly inter-ested in Virtual Organization settings, where groups of agents representing dif-ferent business entities form consortiums that must be regulated by appropriate norms. We are pursuing the concept of an Electronic Institution as a platform providing a normative environment and a set of e-contracting related services. Within this environment, contracts are represented through norms. We intend to test the applicability of our approach through illustration with case-studies and comparison with other contract representation formalisms

Monitoring cooperative business contracts in an institutional environment

The automation of B2B processes is currently a hot research topic. In particular, multi-agent systems have been used to address this arena, where agents can represent enterprises in an interaction environment, automating tasks such as contract negotiation and enactment. Contract monitoring tools are becoming more important as the level of automation of business relationships increase. When business is seen as a joint activity that aims at pursuing a common goal, the successful execution of the contract benefits all involved parties, and thus each of them should try to facilitate the compliance of their partners. Taking into account these concerns and inspecting international legislation over trade procedures, in this paper we present an approach to model contractual obligations: obligations are directed from bearers to counterparties and have flexible deadlines. We formalize the semantics of such obligations using temporal logic, and we provide rules that allow for monitoring them. The proposed implementation is based on a rule-based forward chaining production system

Flexible deadlines for directed obligations in agent-based business contracts

In B2B contract enactment, cooperation should be takeninto account when modeling contractual commitments throughobligations. We advocate a directed deadline obligation approach,taking inspiration on international legislation overtrade procedures. Our proposal is based on authorizationsgranted in specific states of an obligation lifecycle model.Flexible deadlines provide an additional level of cooperationbetween contractual agents. Moreover, agents increase theirdecision-making options concerning obligations

Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval

Smart assistants and recommender systems must deal with lots of information coming from different sources and having different formats. This is more frequent in text data, which presents increased variability and complexity, and is rather common for conversational assistants or chatbots. Moreover, this issue is very evident in the food and nutrition lexicon, where the semantics present increased variability, namely due to hypernyms and hyponyms. This work describes the creation of a set of word embeddings based on the incorporation of information from a food thesaurus - LanguaL - through retrofitting. The ingredients were classified according to three different facet label groups. Retrofitted embeddings seem to properly encode food-specific knowledge, as shown by an increase on accuracy as compared to generic embeddings (+23%, +10% and +31% per group). Moreover, a weighing mechanism based on TF-IDF was applied to embedding creation before retrofitting, also bringing an increase on accuracy (+5%, +9% and +5% per group). Finally, the approach has been tested with human users in an ingredient retrieval exercise, showing very positive evaluation (77.3% of the volunteer testers preferred this method over a string-based matching algorithm)

Towards Enriched Controllability for Educational Question Generation

Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit wh-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question's narrative element. The code is publicly available at github.com/bernardoleite/question-generation-control.Comment: This is a preprint of an article to be published at the Int. Conf. on Artificial Intelligence in Education (AIED, 2023

Electronic institutions with normative environments for agent-based E-contracting

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Inconsistency Detection in Job Postings

The use of AI in recruitment is growing and there is AI software that reads jobs\u27 descriptions in order to select the best candidates for these jobs. However, it is not uncommon for these descriptions to contain inconsistencies such as contradictions and ambiguities, which confuses job candidates and fools the AI algorithm. In this paper, we present a model based on natural language processing (NLP), machine learning (ML), and rules to detect these inconsistencies in the description of language requirements and to alert the recruiter to them, before the job posting is published. We show that the use of an hybrid model based on ML techniques and a set of domain-specific rules to extract the language details from sentences achieves high performance in the detection of inconsistencies